Brazil GIS data
＋ Follow new data418 datasets.
Points of interest: public-facing amenities, shops, tourist attractions, and landmarks tagged via `amenity`, `shop`, `tourism`, or `man_made`. Useful for general POI reference, urban analysis, and basemap detail. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: amenity, shop, tourism, man_made - Part of the country exports on HDX by HOT
Water features across the area: rivers, streams, canals, lakes, ponds, and water bodies tagged with `waterway`, `water`, or `natural=water`. Useful for flood-risk modelling, watershed analysis, and environmental basemap context. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: waterway, water, natural - Part of the country exports on HDX by HOT
Settlements and residential areas: named cities, towns, villages, suburbs, and residential land (`place=*` and `landuse=residential`). Useful for demographic context, settlement-level targeting, and population analysis. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: place, landuse - Part of the country exports on HDX by HOT
Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).
GLIDEnumbers are GLobal unique IDEntifiers for Disasters. Each disaster is registered in a record that contains a GLIDEnumber that uniquely identifies it and allows information to be interoperable across multiple disaster information sources, both structured and textual. Accessing disaster information can be a time consuming and laborious task. Not only is data scattered but frequently identification of the disaster can be confusing in countries with many disaster events and across borders. To address both of these issues, Asian Disaster Reduction Center (ADRC) proposed a globally common Unique ID code for disasters. Among the institutions using actively GLIDEnumbers are UN-OCHA (ReliefWeb, FSCC, HDX), UNDRR, UNDP, WMO, IFRC, FAO, the European Commission and the Centre for Research on the Epidemiology of Disasters (CRED), among many others. Country datasets are only produced for countries with at least one GLIDE event since 1 January 2022. The global dataset contains all events available from the GLIDE API, including those not assigned to a country.
aircraft imagery by Esdras de Lima Andrade, acquired 2025-08-14 to 2025-08-14, 3.5 cm per pixel, published on OpenAerialMap.
Dates of Images: May 1, 2024-May 7, 2024 Summary: Storms that produce heavy rain, damaging winds, hail, and tornadoes often exhibit distinct cloud-top patterns in satellite imagery. Two of these patterns, overshooting tops (OTs) and above-anvil cirrus plumes (AACPs), are frequently observed over intense storms. Using deep learning techniques, researchers at NASA Langley Research Center developed software to automatically detect these severe storm signatures in 500m GOES satellite data collected every 5 minutes. Emergency managers can use this raster data to quickly identify locations where severe storms likely occurred, complementing ground-based reports in regions with limited observations. Severe weather on the ground is more likely when OTs and AACPs are both detected and in areas where detections are clustered or observed repeatedly. Long, nearly straight lines of OT and AACP time-aggregated detections can indicate severe storm tracks. Suggested Use: Near real-time situational awareness about locations where severe storm impacts may have occurred. Example Uses: Target additional information collection. Prioritize field activities (e.g. assessments, resource allocation). Satellite/Sensor/Resolution: GOES-R satellite channels 13 and 15 at approximately 2 km resolution and 5-10 mins. There were some issues with some of the cases GOES files so there are times in which they were skipped. GFS tropopause temperature was also used. Data are all interpolated to 0.5 km resolution. Credits: If you use this software in your project, please contact our team prior to publication and cite our contribution. When using this software, credit the technical background and validation for this software as described here: Cooney, J. W., Bedka, K. M., Liles, C. A., and Homeyer, C. R. (in review). Automated Detection of Overshooting Tops and Above Anvil Cirrus Plumes Within Geostationary Imagery Using Deep Learning. Artificial Intelligence for the Earth Systems. Code is available at https://github.com/nasa/svrstormsig. Esri REST Endpoint: See URL to the right WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2602_202405_FLOOD_BRAZIL/ex2602_severestorm/MapServer/WMSServer
Dates of Images: 20240506T1329, 20240515T1323 (GMT) (YYYYMMDDTHHMM) Summary: The Advanced Rapid Imaging and Analysis (ARIA) and Observational Products for End-Users from Remote Sensing Analysis (OPERA) teams at the Jet Propulsion Laboratory, California Institute of Technology produced maximum vegetation anomaly loss mapping products for hillslopes, with the intent to facilitate detection of landslides, using the OPERA Surface Disturbance Alert from Harmonized Landsat Sentinel-2 (DIST-ALERT-HLS) dataset. The dataset provided includes two product types: Vegetation Disturbance Status maps on individual dates Maximum Vegetation Anomaly loss maps on individual dates Dates and Input Data: Images: May 6, 2024 and May 15, 2024 Products are generated from OPERA DIST-ALERT-HLS satellite imagery and mosaicked across tiles to provide continuous coverage. Product Description:Surface Disturbance, specifically vegetation loss, is identified from optical satellite imagery and mapped at 30 m spatial resolution. The Vegetation Disturbance Status layer indicates the confidence that vegetation loss has occurred, and whether the disturbance is still ongoing or has ended. The Max Vegetation Anomaly loss shows how much vegetation has been lost at each location, based on the largest change observed during the time period. As the number of post-event images increase, the confidence in the detections increases. Note we applied a temporal filter and a slope mask to these products. The temporal filter was used to remove any surface disturbance that occurred prior to the onset of the flooding/rain. The slope mask was used to remove slopes Caveats and Limitations:This product detects vegetation loss, not landslides directly. A slope threshold filter of >8 degrees was applied to exclude gentle terrain where landslides are less likely to occur. Importantly, not all detected changes are landslides—vegetation loss can also result from other causes. These results are intended to support rapid emergency response. All information is provisional for use under emergency response guidelines. These data are provided with absolutely no warranty of any kind. Use at your own risk. Suggested Use: The OPERA DIST-ALERT-HLS maps per pixel vegetation disturbance (specifically, vegetation cover loss) from the Harmonized Landsat Sentinel-2 (HLS) scenes. We provide the (1) maximum vegetation anomaly value (VEG-ANOM-MAX) layer and (2) vegetation disturbance status (VEG-DIST-STATUS). All provided layers have been filtered to include only disturbance detected following the start of flooding/rain. VEG-ANOM-MAX: Difference between historical and current year observed vegetation cover at the date of maximum decrease (vegetation loss of 0-100%). This layer can be used to threshold vegetation disturbance per a given sensitivity (e.g. disturbance of >=20% vegetation cover loss). The sum of the historical percent vegetation and the anomaly value will be the vegetation cover estimate for the current year. VEG-ANOM-MAX Layer Values: File names: ARIA-OPERA_L3_DIST-ALERT-HLS_V1_VEG-ANOM-MAX_20240515T1323_mosaic_SlopeMask_TimeFilt.tif, ARIA-OPERA_L3_DIST-ALERT-HLS_V1_VEG-ANOM-MAX_20240506T1329_mosaic_SlopeMask_TimeFilt.tif 0-100: Maximum loss of percent vegetation (suggested color: white to red gradient) 255: No data (suggested color: transparent) VEG-DIST-STATUS: Indication of vegetation cover loss (vegetation disturbance). The status label is based on the maximum anomaly value, confidence level, and whether it is ongoing or finished. ''First'' means the pixel has had an anomaly detection but no subsequent observations whether anomalous or not. ''Provisional'' means there have been two consecutive disturbance detections but not yet with high confidence. ''Confirmed'' means that vegetation disturbance is detected with high confidence. The label ''finished'' is applied to confirmed disturbances that have had two consecutive no-anomaly observations or one 15 days or more after the last anomaly detection. If a new disturbance is detected, it will overwrite those in a ''finished'' state. These labels are reported for both above and below the 50% disturbance threshold based on the maximum anomaly value. VEG-DIST-STATUS Layer Values: File names: ARIA-OPERA_L3_DIST-ALERT-HLS_V1_VEG-DIST-STATUS_20240506T1329_mosaic_SlopeMask_TimeFilt.tif, ARIA-OPERA_L3_DIST-ALERT-HLS_V1_VEG-DIST-STATUS_20240515T1323_mosaic_SlopeMask_TimeFilt.tif 0: No disturbance (suggested color: #121212) 1: first 2: provisional 3: confirmed 4: first >=50% (suggested color: #008888) 5: provisional >=50% (suggested color: #e48727) 6: confirmed >=50% (suggested color: #e01b07) 7: confirmed 8: confirmed >=50%, finished (suggested color: #dddddd) 255: No data (suggested color: transparent) Satellite/Sensor/Resolution: Satellite/Sensor: Harmonized Landsat Sentinel-2 (HLS) derived from MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B/2C satellites and Operational Land Imager 2 (OLI-2) on NASA/USGS Landsat 8/9 satellites. Spatial Resolution: 30 meters Temporal Resolution: Sub-weekly Credits: The product contains modified Copernicus Sentinel data, processed by the European Space Agency and analyzed by the NASA-JPL/Caltech ARIA/OPERA team. The DIST products are produced as part of the OPERA project, which is funded by NASA to address remote sensing needs identified by the Satellite Needs Working Group, and managed by NASA's Jet Propulsion Laboratory. The DIST-ALERT-HLS product was developed and produced by the OPERA Global Land Analysis and Discovery (GLAD) laboratory at the University of Maryland. NASA JPL-Caltech-UMD ARIA/OPERA Team Product POCs: Cole Speed (cole.speed@jpl.nasa.gov) Mary Grace Bato (bato@jpl.nasa.gov) Emre Havazli (emre.havazli@jpl.nasa.gov) Renato Frasson (renato.prata.de.moraes.frasson@jpl.nasa.gov) Alexander Handwerger (alexander.handwerger@jpl.nasa.gov ) Esri REST Endpoint: See URL to the bottom right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2602_202405_FLOOD_BRAZIL/EX2602_opera_dist_hls/MapServer/WMSServer
Retirement Notice: This item is in mature support as of September 2025 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Brazil Country Boundary provides a 2021 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2021 boundaries are provided by Michael Bauer Research GmbH. They are sourced from 2013 Instituto Brasileiro de Geografia e Estatistica. These were published in October 2022. A new layer will be published in 18 months. Other administrative boundaries for this country are also available: Unidade Municipio Distrito Subdistrito Sectore
uav imagery by GJS CONSULTORIA E PROJETOS, acquired 2026-09-18 to 2026-09-18, 9.4 cm per pixel, published on OpenAerialMap.
uav imagery by Gabriel Mancilla, acquired 2026-08-11 to 2026-08-11, 10.2 cm per pixel, published on OpenAerialMap.
uav imagery by Innagro, acquired 2026-09-11 to 2026-09-11, 7.3 cm per pixel, published on OpenAerialMap.
Dates of Images: 5/6/2024 Summary: The NASA GSFC landslides team produced this landslide polygon map. The landslide map was produced automatically from Planet imagery using the prototype Global Landslide Detection for Rapid response (GLiDEr) system, currently under development as a joint effort between the NASA GSFC landslides team and Data Science group. Pre-event imagery = mosaic of images from 04/20/2024 and 04/21/2024 Post-event imagery = mosaic of images from 05/05/2024 and 05/06/2024 This map has undergone manual false positive removal, but we have not corrected erroneous landslide areas, added missed landslides and resolved amalgamation. Suggested Use: This map can be used to identify location and size of landslides. For landslide mapping extent, refer to the mapping_AOI layer. Credits: NASA GSFC landslides team, NASA GSFC Data Science group, Planet Labs Esri REST Endpoint: See URL on the right side of the page. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2602_202405_FLOOD_BRAZIL/EX2602_planet_landslide_vector/MapServer/WMSServer
Dates of Images: 20240421T1331, 20240506T1329 (GMT) (YYYYMMDDTHHMM) Summary: The Advanced Rapid Imaging and Analysis (ARIA) and Observational Products for End-Users from Remote Sensing Analysis (OPERA) teams at the Jet Propulsion Laboratory, California Institute of Technology produced flood mapping products using the OPERA Dynamic Surface Water Extent from Harmonized Landsat Sentinel-2 (DSWx-HLS) dataset. These products provide a rapid assessment of surface water extent to support emergency response and situational awareness during flood events. The dataset provided includes three products: Surface Water Extent maps on individual dates Water Gain maps, showing areas where water increased between two dates Maximum Surface Water Extent maps, showing the full extent of water observed over a defined time period Dates and Input Data: Pre-event image: April 21, 2024 Flood image: May 6, 2024 Products are generated from OPERA DSWx-HLS satellite imagery and mosaicked across tiles to provide continuous coverage. Product Description:Surface water is identified from optical satellite imagery and mapped at 30 m spatial resolution. Water Gain maps highlight newly inundated areas by comparing pre- and flood-event conditions. Maximum Surface Water Extent maps show all locations where water was detected at any time during the analysis period. Caveats and Limitations:We note the HLS snow/ice mask often misclassifies sediment rich water as snow/ice. Detecting inundation under vegetation, in the urban perimeter, in arid environments, and along water body edges is known to be challenging. The local accuracy of the flood maps in such environments may be impacted. These results are intended to support rapid emergency response. All information is provisional for use under emergency response guidelines. These data are provided with absolutely no warranty of any kind. Use at your own risk. Suggested Use: The OPERA DSWx-HLS Water product classifies the Harmonized Landsat Sentinel-2 (HLS) input imagery into "not water", "open surface water", and “partial surface water”. The "HLS cloud/cloud shadow" and "HLS snow/ice" layers are direct inputs from the HLS FMask. Water (WTR) Layer Values: File names: OPERA_L3_DSWX-HLS_V1_WTR_20240421T1331_mosaic.tif, OPERA_L3_DSWX-HLS_V1_WTR_20240506T1329_mosaic.tif 0: Not Water - an area with valid reflectance data that is not open water (class 1), partial surface water (class 2), snow/ice (class 252), cloud/cloud shadow (class 253), or ocean masked (class 254). Masking can result in a not water (class 0) where land cover masking is applied. (suggested color: #ffffff) 1: Open Water - an area that is entirely water and unobstructed to the sensor, including obstructions by vegetation, terrain, and buildings. (suggested color: #0000ff) 2: Partial Surface Water - an inundated area that is at least 20% and less than 100% open water. This may be referred to as “subpixel inundation” when referring to a pixel’s area. Examples include wetlands, water bodies with emergent vegetation, and pixels bisected by coastlines. (suggested color: #b4d5f4) 252: Snow/Ice/Sediment Rich Water - an area identified as snow/ice according to input HLS Fmask quality assurance (QA) data (suggested color: #00ffff) 253: Cloud/Cloud Shadow - an area identified as cloud or cloud shadow or adjacent to cloud/cloud shadow according to input HLS Fmask quality assurance (QA) data (suggested color: #afafaf) 254: Ocean Masked - an area identified as ocean using a shoreline database with an added margin (suggested color: #00007f) 255: No data (suggested color: transparent) Water Gain Layer Values: The water gain shows any location where there was a change from “not water” to “partial surface water”, “not water” to “open surface water”, or “partial surface water” to “open surface water" between two OPERA DSWx-HLS products. File names: ARIA-OPERA_L3_DSWX-HLS_V1_WTR_20240421T1331_20240506T1329_water_gain 0: No Change or Water Loss- an area that had no change (e.g., open water in both images) or water loss (e.g., open water to not water). Masked pixels are set to No data. (suggested color: #ffffff) 1: Water Gain - an area that gained water in the most recent image. This includes change from Not Water to Partial Surface Water, Not Water to Open Water, and Partial Surface Water to Open Water. (suggested color: #ff15e4) Maximum Water Extent Layer Values: The maximum water extent observed during the event was created by aggregating OPERA DSWx-HLS water products. File names: ARIA-OPERA_L3_DSWX-HLS_V1_WTR_20240421T1340_20240506T1329_max_extent.tif 0: Not Water. No water was detected within the time period of the images. Masked pixels are set to No data. (suggested color: #ffffff) 1: Water - an area where open water or partial surface water was detected during the time period of the images. (suggested color: #ff15e4) Satellite/Sensor/Resolution: Satellite/Sensor: Harmonized Landsat Sentinel-2 (HLS) derived from MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B/2C satellites and Operational Land Imager 2 (OLI-2) on NASA/USGS Landsat 8/9 satellites. Spatial Resolution: 30 meters Temporal Resolution: Sub-weekly Credits: The product contains modified Copernicus Sentinel data, processed by the European Space Agency and analyzed by the NASA-JPL/Caltech ARIA/OPERA team. The DSWx products are produced as part of the OPERA project, which is funded by NASA to address remote sensing needs identified by the Satellite Needs Working Group, and managed by NASA's Jet Propulsion Laboratory. Product POCs: Cole Speed (cole.speed@jpl.nasa.gov) Mary Grace Bato (bato@jpl.nasa.gov) Emre Havazli (emre.havazli@jpl.nasa.gov) Renato Frasson (renato.prata.de.moraes.frasson@jpl.nasa.gov) Alexander Handwerger (alexander.handwerger@jpl.nasa.gov ) Esri REST Endpoint: See URL to the bottom right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2602_202405_FLOOD_BRAZIL/EX2602_opera_dswx_hls/MapServer/WMSServer
uav imagery by GJS CONSULTORIA E PROJETOS, acquired 2026-09-09 to 2026-09-09, 9.4 cm per pixel, published on OpenAerialMap.
uav imagery by GJS CONSULTORIA E PROJETOS, acquired 2025-03-28 to 2025-03-28, 10.0 cm per pixel, published on OpenAerialMap.
Dates of Images: Post-Event: 5/8/2024; 5/15/2024 Pre-Event: 4/6/2024; 4/21/2024 Date of Next Image: Unknown Summary: Normalized Differential Vegetation Index (NDVI) maps are produced by converting pixel values to NDVI = (Red – NIR)/(Red + NIR), NIR indicates the near infrared band. The dynamically generated Normalized Difference Vegetation Index (NDVI) layer is an index for quantifying green vegetation. It reflects the state of vegetation health based on how vegetation reflects light at certain wavelengths. NDVI change was created by subtracting the cloud-masked post-event NDVI from the cloud-masked pre-event NDVI to show areas of potentially impacted vegetation as negative values (darker shades) and healthy/unimpacted vegetation in positive values (lighter shades). NDVI change can give insights to impacts from an event (like the scouring of vegetation from the riverbeds) but can also show changes in unrelated areas like the clearing of agriculture fields. This layer is a binary NDVI change polygon (threshold of areas where NDVI decreased by -0.25 or more) for September 19, 2018 as compared to August 9, 2018. Suggested Use: NDVI change: Commonly used to distinguish healthy/active vegetation (positive values) from potentially unhealthy/impacted vegetation (negative values). This map shows a binary flag where NDVI decreased. Satellite/Sensor: Landsat Resolution: 3 meters Credits: NASA/MSFC, USGS Esri REST Endpoint: See URL section on right side of page WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2602_202405_FLOOD_BRAZIL/ex2602_landsat_bndvi/MapServer/WMSServer
uav imagery by GJS CONSULTORIA E PROJETOS, acquired 2025-10-17 to 2025-10-17, 9.4 cm per pixel, published on OpenAerialMap.
uav imagery by GJS CONSULTORIA E PROJETOS, acquired 2026-02-07 to 2026-02-07, 4.7 cm per pixel, published on OpenAerialMap.
uav imagery by Gabriel Mancilla, acquired 2026-08-26 to 2026-08-26, 5.4 cm per pixel, published on OpenAerialMap.
uav imagery by Gabriel Mancilla, acquired 2026-03-14 to 2026-03-14, 7.1 cm per pixel, published on OpenAerialMap.
uav imagery by Victor Gomes, acquired 2026-05-07 to 2026-05-07, 3.3 cm per pixel, published on OpenAerialMap.
uav imagery by Gabriel Mancilla, acquired 2026-05-24 to 2026-05-24, 13.8 cm per pixel, published on OpenAerialMap.
